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Driving under varying road conditions is challenging, especially forautonomous vehicles that must adapt in real-time to changes in the environment,e.g., rain, snow, etc. It is difficult to apply offline learning-based methodsin these time-varying settings, as the controller should be trained on datasetsrepresenting all conditions it might encounter in the future. While onlinelearning may adapt a model from real-time data, its convergence is often tooslow for fast varying road conditions. We study this problem in autonomousracing, where driving at the limits of handling under varying road conditionsis required for winning races. We propose a computationally-efficient approachthat leverages an ensemble of Gaussian processes (GPs) to generalize and adaptpre-trained GPs to unseen conditions. Each GP is trained on driving data with adifferent road surface friction. A time-varying convex combination of these GPsis used within a model predictive control (MPC) framework, where the modelweights are adapted online to the current road condition based on real-timedata. The predictive variance of the ensemble Gaussian process (EGP) modelallows the controller to account for prediction uncertainty and enables safeautonomous driving. Extensive simulations of a full scale autonomous cardemonstrated the effectiveness of our proposed EGP-MPC method for providinggood tracking performance in varying road conditions and the ability togeneralize to unknown maps.

Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces